Category Archives: Learning

Learning information is different from communicating understanding

Elon Musk’s Neuralink vision is highspeed multi-port access to the stored understanding in one mind (via an array of electrodes on the cortex), and a mind-mapping translation engine which can copy the structure of “understanding” in that one brain, and then writing that “understanding” into to a destination brain via its own array of electrodes, hence transmitting a specific understanding. Elon Musk believes that this will be much faster than communicating via language. I believe that he is mistaken.

Learning in its fullest sense remains a huge mystery. The process of understanding something rarely leaves us with any insight into how we achieved it. It is not obvious how we will gain better insight into the process of learning and understanding, whether through neuro-science or through psychology, Currently neither seem close to revealing how we do it.

Even the best teachers lack a scientific description of their method. Education which goes beyond just information, is a rather like mysterious religious ritual or a cargo cult: we are familiar with many of the useful parts of the ritual we surround the learner with, but not much more. I guess that in order to find faster ways of communicating “understanding”, we must gain a scientific understanding of what “understanding” is.

I was always fascinated by the paradox of how difficult I found an idea before I got it, then when I understood it, I couldn’t remember what had been the problem. It is conceivable that “understanding” is an intrinsically slow process, limited by our cerebral hardware capabilities, and that our frustration comes from the idea that it should be simple because it seems simple in retrospect. However, we might have evolved to delete all memory of the process each time we acquire insight, deeming that to be wasteful data to store. Our retrospective idea of the simplicity of an “understanding” may be a total falsehood.

Although I don’t claim our brain is a computer, I like the idea that we run an internal simulation of “what’s out there” based on our previous experience. From memories of fibre system simulators, one can expect a more comprehensive simulation (bigger world-view) to run slower than a simpler one, and that is what we find with humans versus apes in learning speed. The endorphin boost I get from understanding something leads me to believe that we have evolved to receive a reward when we compress data into insights, as insights (understandings) are our powerful predictive tools. The more elegant the simplification to a concept, the bigger the thrill! A reward for minimising the memory requirement and maximising the power of the predictive tool.

We know that enabling AI systems to develop insights is proving very difficult. When we humans recognise an object in 3D space we are using so much of our previous experience to predict the most likely solution. I think that our own illusion of having a rich experience in the present moment deceives us into expecting an AI system to be able to achieve the same in real time. It is possible that even an infinitely fast processor will be intrinsically unable to solve some recognition problems in real time. Yet the evidence (presented in Bottleneck) suggests that for humans, real time is completely insufficient for the task, it is years of experience which enables us to accurately the predict the present, the future and the past in such detail. So progress in AI might be greater if we understood more of how our brains continually evolve a more comprehensive world view, based on understanding and metaphor.

Elon Musk’s new venture into electronic-brain interfaces will hopefully transform the lives of those with disabilities. However, it will ultimately disappoint those who dream of transhuman cyborgs with superpowers. While Artificial Intelligent machines may evolve along a Moore’s Law trajectory, we humans will just have to make the best of the antiquated brain technology we are born with, finding increasingly clever ways to use its incredible capabilities.

Elon Musk thinks that accessing the brain directly will enable us to interact faster with AI. He is making a fundamental error.

Speaking about AI at the Code Conference 2016, he said: “Constrained by input output”, “If we can create a high-bandwidth neural interface with your digital self”, “Access directly to cortex”, “How to establish a high bandwidth neural interface”

Unfortunately Elon Musk does not realise that the bottleneck is the brain’s ability to integrate new information. Our sensors already have greater input capacity than our biology based brains can process, so unless we replace the CPU (the brain), we will never be superhumans.

The truth about our learning bottleneck

We often hear arguments about the percentage that we are defined by the nature we are born with, and the percentage that we are defined by our experiences from our birth onward. The two sides sometimes agree a truce saying its 50% of each, which I think is a way of avoiding examining what is meant and in what context. So here are a few thoughts and observations:

Nature: If we believe that evolution has largely directed our genetic make up, then we must expect any general trends to be primarily optimised for early man, rather than for civilised 21st century man, indeed the majority of our DNA is identical to that of our earliest Homo sapiens ancestors, and is remarkably similar to that of our ape relatives. The kind of pre-programmed behaviours that we are born with are those which maximise the survival of an ape in its first few years at most. Evolutionary forces have had little exposure to the more recent activities of our species, so we cannot expect evolution to have optimised specific activities such as flaking a flint tool or playing tennis, but could reasonable expect an optimisation of physical skill in general. What about differences or defects in our DNA? These certainly can effect our physical characteristics, and often do so conspicuously through genetic diseases. However there seems to be little evidence that aspects of psychological personality are encoded in our genes and inherited.

Nurture: A newborn baby knows almost nothing of the world it finds itself in, and must build an internal idea of what is out there through observation and experience. Being incapable of survival alone, it builds its idea of the world through interaction with parents and other individuals. It is increasingly acknowledged that the entire world that we experience, is an internal construct, our best guess to date of what is out there. If we consider just how rich that experience is for adults, then we can see that almost all our daily experience is derived from what we have learned in the intervening years. The quantity of information that we subsequently learn through our lifetime completely dwarfs that which we are born with. Our behaviours are therefore likely to be dominated by what we have learned since our birth.

It is interesting to compare the inherited information with which we are born, with the very basic operating system (or BIOS) installed within a PC during its manufacture. When a PC is first switched on, the BIOS provides it with sufficient intelligence to be able to subsequently load and run a complex operating system such as Windows, and various additional programs for dealing with email or word processing. Like a new-born baby, basic PCs are fairly dumb but have huge potential. While the physical architecture (and technology) of most PCs is almost identical (as is also the case with humans), a PC can only go on to perform highly complex tasks when it has acquired such programs and additional data. The difference is that until now PCs are almost entirely force-fed with software and information, while exploration and discovery places a huge role in human learning.

So my take on this is that our specific behaviours are almost completely dominated by nurture, while our nature may influence our tendencies. Of course my deductive software may be wrong.